diff --git a/cami_src/cami_suite.py b/cami_src/cami_suite.py
index a7df6f7a18a41010cd5d3fd4b2a65a4dc08d9c0c..4825d6261b9f7280bd17d38c8c225a9ab085e8c2 100644
--- a/cami_src/cami_suite.py
+++ b/cami_src/cami_suite.py
@@ -342,7 +342,7 @@ class cami():
         # transform all vertex indices to their corresponding gene names in a result set
         for tool in result_sets:
             self.result_gene_sets[tool.name] = set([gene_name_map[vertex] for vertex in result_sets[tool]])
-                
+        
         # create integer codes for cami_versions (needed for predicted_by vertex property)
         recursion_limit = sys.getrecursionlimit()
         for cami_method_name, cami_params in camis.items():
@@ -374,20 +374,15 @@ class cami():
             # for visualization with nvenn
             self.result_gene_sets[cami_method_name] = set(cami_genes)
             
-            
-            # transform all vertex indices to their corresponding gene names in a result set
-            for tool in result_sets:
-                self.result_gene_sets[tool.name] = set([gene_name_map[vertex] for vertex in result_sets[tool]])
-                
-            # add seeds to result sets for drugstone and digest
-            for toolname in self.result_gene_sets:
-                self.result_module_sets[toolname] = self.result_gene_sets[toolname].union(set([gene_name_map[svertex] for svertex in self.seed_lst]))
-                print(f'With the {len(seed_genes)} seed genes the module predicted by {toolname} contains {len(self.result_module_sets[toolname])} genes')
-
             sys.setrecursionlimit(recursion_limit)
             # save the results in outputfiles
             self.generate_output(cami_method_name, seed_genes, cami_vlist, cami_vertices, putative_vertices, cami_genes,
                                  gene_name_map, codes2tools, cami_scores)
+            
+         # add seeds to result sets for drugstone and digest
+        for toolname in self.result_gene_sets:
+            self.result_module_sets[toolname] = self.result_gene_sets[toolname].union(set([gene_name_map[svertex] for svertex in self.seed_lst]))
+            print(f'With the {len(seed_genes)} seed genes the module predicted by {toolname} contains {len(self.result_module_sets[toolname])} genes')
 
     def generate_output(self, cami_method, seed_genes, cami_vlist, cami_vertices, putative_vertices, cami_genes,
                         gene_name_map, codes2tools, cami_scores):